code: def tokvecs(docs, vector_size):
      output = []
      for doc in docs:
          vec = numpy.random.uniform(-0.1, 0.1, (len(doc), vector_size))
          output.append(numpy.asarray(vec))
      return output
  
imports: import hypothesis
  import hypothesis.strategies
  import numpy
  import pytest
  from thinc.tests.strategies import ndarrays_of_shape
  from spacy.language import Language
  from spacy.pipeline._parser_internals._beam_utils import BeamBatch
  from spacy.pipeline._parser_internals.arc_eager import ArcEager
  from spacy.pipeline._parser_internals.stateclass import StateClass
  from spacy.tokens import Doc
  from spacy.training import Example
  from spacy.vocab import Vocab
label_desc: Use image node_id 6 for calling a global function with example usage: tokvecs(docs, vector_size) and returns: output